------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Volumes/rdss_meghrogers/Current Research/Markets-Pridemore/Work/MAR05-QuantModels-UNODC.log
  log type:  text
 opened on:   5 May 2025, 14:46:51

. 
. //  program:    Stata 
. //  task:       Quantile Regression for Panel Data
. //  project:    Markets  
. 
. version
version 18.5

. clear all

. macro drop _all

. set linesize 80

. set more off

. local tag " 06-25-24| Cleaned 06-25-24"

. local file "MAR05-QuantModels-UNODC"

. local note "|`tag' | `file'"

. local opt "noparen sideway excel noaster  bdec(2)  sdec(2)  pdec(3)   adec(2) 
> e(r2) stats(coef se pval)"

. local dv "lrhom_un"

. local iv "fraser"

. local iv2 "infantmort" 

. local cont "edu unemp  popdense perurban  sexratio"

. 
. //      #0
. //      Loading data 
. use MAR04-ModModels-UNODC.dta, clear 

. 
. //      #1
. //      Fraser quantile regression for panel models 
. foreach quant in  25 50 75  {
  2.         qregpd `dv' `iv' `iv2' `cont' , quantile(0.`quant') id(CID) fix(yea
> r)
  3.         estimates store q1_`quant'
  4.         }
Nelder-Mead optimization
Initial:      f(p) = -88.347978
Rescale:      f(p) = -38.179985
Iteration 0:  f(p) = -38.179985  
Iteration 1:  f(p) = -12.112036  
Iteration 2:  f(p) = -12.112036  
Iteration 3:  f(p) = -12.112036  
Iteration 4:  f(p) = -12.112036  
Iteration 5:  f(p) = -5.1040995  
Iteration 6:  f(p) = -5.1040995  
Iteration 7:  f(p) = -4.0502696  
Iteration 8:  f(p) = -3.8082369  
Iteration 9:  f(p) = -3.8082369  
Iteration 10: f(p) = -3.8082369  
Iteration 11: f(p) = -3.8082369  
Iteration 12: f(p) = -3.8082369  
Iteration 13: f(p) = -3.8082369  
Iteration 14: f(p) = -3.8082369  
Iteration 15: f(p) = -3.8082369  
Iteration 16: f(p) = -3.8082369  
Iteration 17: f(p) = -3.8082369  
Iteration 18: f(p) = -3.8082369  
Iteration 19: f(p) = -3.8082369  
Iteration 20: f(p) = -3.8082369  
Iteration 21: f(p) = -3.8082369  
Iteration 22: f(p) = -3.8082369  
Iteration 23: f(p) = -3.8082369  
Iteration 24: f(p) = -3.8082369  
Iteration 25: f(p) = -3.8082369  
Iteration 26: f(p) = -3.8082369  


Quantile Regression for Panel Data (QRPD)
     Number of obs:              1949
     Number of groups:            123
     Min obs per group:             5
     Max obs per group:            20
------------------------------------------------------------------------------
    lrhom_un | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      fraser |  -.5217217   776.4146    -0.00   0.999    -1522.266    1521.223
  infantmort |   5.442277   20.78038     0.26   0.793    -35.28652    46.17107
         edu |  -3.839446   16698.82    -0.00   1.000    -32732.93    32725.26
       unemp |  -6.526203   461.2354    -0.01   0.989    -910.5311    897.4787
    popdense |   11.03915   30.76971     0.36   0.720    -49.26838    71.34667
    perurban |   8.521242   117.0966     0.07   0.942    -220.9839    238.0264
    sexratio |  -5.806226   134.5032    -0.04   0.966    -269.4277    257.8153
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.
Nelder-Mead optimization
Initial:      f(p) = -120.86162
Rescale:      f(p) = -28.877613
Iteration 0:  f(p) = -28.877613  
Iteration 1:  f(p) = -28.877613  
Iteration 2:  f(p) = -28.877613  
Iteration 3:  f(p) = -28.877613  
Iteration 4:  f(p) = -28.877613  
Iteration 5:  f(p) = -28.877613  
Iteration 6:  f(p) = -28.877613  
Iteration 7:  f(p) = -28.877613  
Iteration 8:  f(p) = -28.877613  
Iteration 9:  f(p) = -28.877613  
Iteration 10: f(p) = -28.877613  
Iteration 11: f(p) = -27.185341  
Iteration 12: f(p) = -27.185341  
Iteration 13: f(p) = -27.185341  
Iteration 14: f(p) = -27.185341  
Iteration 15: f(p) =  -11.05561  
Iteration 16: f(p) =  -11.05561  
Iteration 17: f(p) =  -11.05561  
Iteration 18: f(p) = -8.9394735  
Iteration 19: f(p) = -8.9394735  
Iteration 20: f(p) = -8.9394735  
Iteration 21: f(p) = -8.9394735  
Iteration 22: f(p) = -8.9394735  
Iteration 23: f(p) = -8.9394735  
Iteration 24: f(p) = -6.1779459  
Iteration 25: f(p) = -6.1779459  
Iteration 26: f(p) = -6.1779459  
Iteration 27: f(p) =  -3.814305  
Iteration 28: f(p) =  -3.814305  
Iteration 29: f(p) = -2.3888119  
Iteration 30: f(p) = -2.3888119  
Iteration 31: f(p) = -2.2218163  
Iteration 32: f(p) = -2.2218163  
Iteration 33: f(p) = -2.2218163  
Iteration 34: f(p) = -2.2218163  
Iteration 35: f(p) = -2.2218163  
Iteration 36: f(p) = -2.2218163  
Iteration 37: f(p) = -2.2218163  
Iteration 38: f(p) = -2.2218163  
Iteration 39: f(p) = -2.2218163  
Iteration 40: f(p) = -2.2218163  
Iteration 41: f(p) = -2.2218163  
Iteration 42: f(p) = -2.2218163  
Iteration 43: f(p) = -2.1503434  
Iteration 44: f(p) = -2.1503434  
Iteration 45: f(p) = -2.1503434  
Iteration 46: f(p) = -2.1503434  
Iteration 47: f(p) = -2.1503434  
Iteration 48: f(p) = -2.1503434  
Iteration 49: f(p) = -2.1503434  
Iteration 50: f(p) = -2.1503434  
Iteration 51: f(p) = -2.1503434  
Iteration 52: f(p) = -2.1503434  
Iteration 53: f(p) = -2.1503434  


Quantile Regression for Panel Data (QRPD)
     Number of obs:              1949
     Number of groups:            123
     Min obs per group:             5
     Max obs per group:            20
------------------------------------------------------------------------------
    lrhom_un | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      fraser |   .0065768   .2407337     0.03   0.978    -.4652525    .4784061
  infantmort |  -.0045024   .0068551    -0.66   0.511    -.0179382    .0089334
         edu |  -.9745766   2.007232    -0.49   0.627    -4.908679    2.959526
       unemp |   .0129116   .0112923     1.14   0.253    -.0092209    .0350441
    popdense |   .0020195   .0011095     1.82   0.069    -.0001551     .004194
    perurban |   .0048584   .0102705     0.47   0.636    -.0152715    .0249883
    sexratio |  -.0018205   .0134102    -0.14   0.892     -.028104     .024463
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.
Nelder-Mead optimization
Initial:      f(p) = -82.967269
Rescale:      f(p) = -22.862501
Iteration 0:  f(p) = -22.862501  
Iteration 1:  f(p) =  -6.190379  
Iteration 2:  f(p) =  -6.190379  
Iteration 3:  f(p) = -2.1788784  
Iteration 4:  f(p) = -2.1788784  
Iteration 5:  f(p) = -2.1788784  
Iteration 6:  f(p) = -2.1788784  
Iteration 7:  f(p) =   -.719822  
Iteration 8:  f(p) =   -.719822  
Iteration 9:  f(p) =   -.719822  
Iteration 10: f(p) =   -.719822  
Iteration 11: f(p) =   -.719822  
Iteration 12: f(p) =   -.719822  
Iteration 13: f(p) =   -.719822  
Iteration 14: f(p) =   -.719822  
Iteration 15: f(p) =   -.719822  
Iteration 16: f(p) =   -.719822  


Quantile Regression for Panel Data (QRPD)
     Number of obs:              1949
     Number of groups:            123
     Min obs per group:             5
     Max obs per group:            20
------------------------------------------------------------------------------
    lrhom_un | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      fraser |   1.167633          .        .       .            .           .
  infantmort |     1.2384    1.33112     0.93   0.352    -1.370546    3.847347
         edu |  -2.718852          .        .       .            .           .
       unemp |   1.273852   3.334299     0.38   0.702    -5.261255    7.808958
    popdense |   6.249821   2.833505     2.21   0.027     .6962537    11.80339
    perurban |  -4.368309   1.788423    -2.44   0.015    -7.873554   -.8630646
    sexratio |   1.241636   .7925557     1.57   0.117    -.3117445    2.795017
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.

. estimates table q1_25 q1_50 q1_75, b(%7.2f) se(%7.2f) p(%4.3f)

--------------------------------------------
    Variable |  q1_25     q1_50     q1_75   
-------------+------------------------------
      fraser |   -0.52      0.01      1.17  
             |  776.41      0.24      0.00  
             |   0.999     0.978         .  
  infantmort |    5.44     -0.00      1.24  
             |   20.78      0.01      1.33  
             |   0.793     0.511     0.352  
         edu |   -3.84     -0.97     -2.72  
             | 16698.82      2.01      0.00  
             |   1.000     0.627         .  
       unemp |   -6.53      0.01      1.27  
             |  461.24      0.01      3.33  
             |   0.989     0.253     0.702  
    popdense |   11.04      0.00      6.25  
             |   30.77      0.00      2.83  
             |   0.720     0.069     0.027  
    perurban |    8.52      0.00     -4.37  
             |  117.10      0.01      1.79  
             |   0.942     0.636     0.015  
    sexratio |   -5.81     -0.00      1.24  
             |  134.50      0.01      0.79  
             |   0.966     0.892     0.117  
--------------------------------------------
                              Legend: b/se/p

. 
. //      #2
. //      Heritage quantile regression for panel models 
. foreach quant in  25 50 75  {
  2.         qregpd `dv' heritage `iv2' `cont' , quantile(0.`quant') id(CID) fix
> (year)
  3.         estimates store q2_`quant'
  4.         }
Nelder-Mead optimization
Initial:      f(p) = -113.31816
Rescale:      f(p) = -48.368818
Iteration 0:  f(p) = -48.368818  
Iteration 1:  f(p) = -17.762373  
Iteration 2:  f(p) = -17.762373  
Iteration 3:  f(p) = -.74206132  
Iteration 4:  f(p) = -.74206132  
Iteration 5:  f(p) = -.74206132  
Iteration 6:  f(p) = -.74206132  
Iteration 7:  f(p) = -.74206132  
Iteration 8:  f(p) = -.74206132  
Iteration 9:  f(p) = -.74206132  
Iteration 10: f(p) = -.74206132  
Iteration 11: f(p) = -.74206132  
Iteration 12: f(p) = -.74206132  
Iteration 13: f(p) = -.74206132  
Iteration 14: f(p) = -.74206132  
Iteration 15: f(p) = -.74206132  
Iteration 16: f(p) = -.74206132  
Iteration 17: f(p) = -.74206132  
Iteration 18: f(p) = -.74206132  
Iteration 19: f(p) = -.74206132  
Iteration 20: f(p) = -.74206132  
Iteration 21: f(p) = -.74206132  
Iteration 22: f(p) = -.09815555  
Iteration 23: f(p) = -.09815555  
Iteration 24: f(p) = -.09815555  
Iteration 25: f(p) = -.09815555  
Iteration 26: f(p) = -.09815555  
Iteration 27: f(p) = -.09815555  
Iteration 28: f(p) = -.09815555  
Iteration 29: f(p) = -.09815555  
Iteration 30: f(p) = -.09815555  
Iteration 31: f(p) = -.09815555  
Iteration 32: f(p) = -.09815555  


Quantile Regression for Panel Data (QRPD)
     Number of obs:              1878
     Number of groups:            120
     Min obs per group:             2
     Max obs per group:            20
------------------------------------------------------------------------------
    lrhom_un | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
    heritage |  -2.428878   21.88883    -0.11   0.912     -45.3302    40.47245
  infantmort |   7.706233   9.980261     0.77   0.440    -11.85472    27.26718
         edu |   4.814227          .        .       .            .           .
       unemp |  -4.864434   50.24563    -0.10   0.923    -103.3441    93.61518
    popdense |   6.425822    11.1432     0.58   0.564    -15.41445     28.2661
    perurban |   6.548662   8.756537     0.75   0.455    -10.61384    23.71116
    sexratio |  -17.04997   26.61764    -0.64   0.522    -69.21958    35.11964
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.
Nelder-Mead optimization
Initial:      f(p) = -145.50697
Rescale:      f(p) = -145.50697
Iteration 0:  f(p) = -145.50697  
Iteration 1:  f(p) = -92.734531  
Iteration 2:  f(p) = -51.756619  
Iteration 3:  f(p) = -51.756619  
Iteration 4:  f(p) = -51.756619  
Iteration 5:  f(p) = -51.756619  
Iteration 6:  f(p) = -51.756619  
Iteration 7:  f(p) = -51.756619  
Iteration 8:  f(p) = -51.756619  
Iteration 9:  f(p) = -51.756619  
Iteration 10: f(p) =  -25.27082  
Iteration 11: f(p) = -24.682399  
Iteration 12: f(p) = -24.682399  
Iteration 13: f(p) = -18.588669  
Iteration 14: f(p) = -18.588669  
Iteration 15: f(p) = -18.588669  
Iteration 16: f(p) = -18.588669  
Iteration 17: f(p) = -18.588669  
Iteration 18: f(p) = -17.688661  
Iteration 19: f(p) = -17.688661  
Iteration 20: f(p) = -17.688661  
Iteration 21: f(p) = -17.688661  
Iteration 22: f(p) = -16.184439  
Iteration 23: f(p) = -16.184439  
Iteration 24: f(p) = -16.184439  
Iteration 25: f(p) = -16.184439  
Iteration 26: f(p) = -16.184439  
Iteration 27: f(p) = -16.184439  
Iteration 28: f(p) = -16.184439  
Iteration 29: f(p) = -16.184439  
Iteration 30: f(p) = -16.184439  
Iteration 31: f(p) = -16.184439  
Iteration 32: f(p) = -16.184439  
Iteration 33: f(p) = -16.184439  


Quantile Regression for Panel Data (QRPD)
     Number of obs:              1878
     Number of groups:            120
     Min obs per group:             2
     Max obs per group:            20
------------------------------------------------------------------------------
    lrhom_un | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
    heritage |  -1.455053   60.41286    -0.02   0.981    -119.8621     116.952
  infantmort |   9.746455   104.1794     0.09   0.925    -194.4415    213.9344
         edu |  -2.991198   519.5212    -0.01   0.995    -1021.234    1015.252
       unemp |  -.9193921   16.04735    -0.06   0.954    -32.37162    30.53283
    popdense |   10.14439   122.7561     0.08   0.934    -230.4531    250.7419
    perurban |  -1.998139   124.1994    -0.02   0.987    -245.4246    241.4283
    sexratio |   2.006313   139.9874     0.01   0.989    -272.3639    276.3766
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.
Nelder-Mead optimization
Initial:      f(p) = -101.00141
Rescale:      f(p) = -35.088529
Iteration 0:  f(p) = -35.088529  
Iteration 1:  f(p) = -14.230995  
Iteration 2:  f(p) = -14.230995  
Iteration 3:  f(p) = -3.7875977  
Iteration 4:  f(p) = -3.7875977  
Iteration 5:  f(p) = -3.7863703  
Iteration 6:  f(p) = -3.7863703  
Iteration 7:  f(p) = -3.2292736  
Iteration 8:  f(p) = -2.0487265  
Iteration 9:  f(p) =  -1.683772  
Iteration 10: f(p) =  -1.683772  
Iteration 11: f(p) = -1.4938531  
Iteration 12: f(p) = -1.4938531  
Iteration 13: f(p) = -1.4938531  
Iteration 14: f(p) = -1.4938531  


Quantile Regression for Panel Data (QRPD)
     Number of obs:              1878
     Number of groups:            120
     Min obs per group:             2
     Max obs per group:            20
------------------------------------------------------------------------------
    lrhom_un | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
    heritage |   3.011549   4.675532     0.64   0.520    -6.152325    12.17542
  infantmort |   .5045647    1.77417     0.28   0.776    -2.972744    3.981874
         edu |  -3.590948          .        .       .            .           .
       unemp |   .5522926   5.013148     0.11   0.912    -9.273297    10.37788
    popdense |   6.453905   8.488254     0.76   0.447    -10.18277    23.09058
    perurban |  -.3955362   3.475784    -0.11   0.909    -7.207947    6.416874
    sexratio |  -1.299221   13.33531    -0.10   0.922    -27.43596    24.83752
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.

. estimates table q2_25 q2_50 q2_75, b(%7.2f) se(%7.2f) p(%4.3f)

--------------------------------------------
    Variable |  q2_25     q2_50     q2_75   
-------------+------------------------------
    heritage |   -2.43     -1.46      3.01  
             |   21.89     60.41      4.68  
             |   0.912     0.981     0.520  
  infantmort |    7.71      9.75      0.50  
             |    9.98    104.18      1.77  
             |   0.440     0.925     0.776  
         edu |    4.81     -2.99     -3.59  
             |    0.00    519.52      0.00  
             |       .     0.995         .  
       unemp |   -4.86     -0.92      0.55  
             |   50.25     16.05      5.01  
             |   0.923     0.954     0.912  
    popdense |    6.43     10.14      6.45  
             |   11.14    122.76      8.49  
             |   0.564     0.934     0.447  
    perurban |    6.55     -2.00     -0.40  
             |    8.76    124.20      3.48  
             |   0.455     0.987     0.909  
    sexratio |  -17.05      2.01     -1.30  
             |   26.62    139.99     13.34  
             |   0.522     0.989     0.922  
--------------------------------------------
                              Legend: b/se/p

. 
. 
. //      #3
. //      ROL quantile regression for panel models 
. foreach quant in  25 50 75  {
  2.         qregpd `dv' rol `iv2' `cont' , quantile(0.`quant') id(CID) fix(year
> )
  3.         estimates store q3_`quant'
  4.         }
Nelder-Mead optimization
Initial:      f(p) = -113.30572
Rescale:      f(p) = -113.30572
Iteration 0:  f(p) = -113.30572  
Iteration 1:  f(p) = -16.568868  
Iteration 2:  f(p) = -16.568868  
Iteration 3:  f(p) = -16.568868  
Iteration 4:  f(p) = -16.568868  
Iteration 5:  f(p) = -16.568868  
Iteration 6:  f(p) = -16.568868  
Iteration 7:  f(p) = -16.568868  
Iteration 8:  f(p) = -16.568868  
Iteration 9:  f(p) = -12.122713  
Iteration 10: f(p) = -12.122713  
Iteration 11: f(p) = -12.122713  
Iteration 12: f(p) = -12.122713  
Iteration 13: f(p) = -12.122713  
Iteration 14: f(p) = -12.122713  
Iteration 15: f(p) =  -11.90286  
Iteration 16: f(p) = -9.2961112  
Iteration 17: f(p) = -9.2961112  
Iteration 18: f(p) = -6.4489623  
Iteration 19: f(p) = -6.4489623  
Iteration 20: f(p) = -6.4489623  
Iteration 21: f(p) = -6.3444344  
Iteration 22: f(p) = -6.3444344  
Iteration 23: f(p) = -6.3444344  
Iteration 24: f(p) = -6.3444344  
Iteration 25: f(p) = -6.3444344  
Iteration 26: f(p) = -6.3444344  
Iteration 27: f(p) = -6.3444344  
Iteration 28: f(p) = -6.3444344  
Iteration 29: f(p) = -6.3444344  
Iteration 30: f(p) = -6.3444344  
Iteration 31: f(p) = -6.3444344  
Iteration 32: f(p) = -6.3444344  
Iteration 33: f(p) = -6.3444344  
Iteration 34: f(p) = -6.3444344  
Iteration 35: f(p) = -6.3444344  
Iteration 36: f(p) = -6.3444344  
Iteration 37: f(p) = -6.3444344  
Iteration 38: f(p) = -6.3444344  
Iteration 39: f(p) = -6.3444344  
Iteration 40: f(p) = -6.3444344  


Quantile Regression for Panel Data (QRPD)
     Number of obs:              1789
     Number of groups:            123
     Min obs per group:             5
     Max obs per group:            18
------------------------------------------------------------------------------
    lrhom_un | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         rol |    2.10988   37.84252     0.06   0.956    -72.06009    76.27985
  infantmort |   5.829894   8.001662     0.73   0.466    -9.853076    21.51286
         edu |   1.034411   742.5205     0.00   0.999    -1454.279    1456.348
       unemp |  -1.376992   6.404301    -0.22   0.830    -13.92919    11.17521
    popdense |   .9199542    1.06083     0.87   0.386    -1.159235    2.999144
    perurban |  -3.864641   6.218569    -0.62   0.534    -16.05281     8.32353
    sexratio |   1.083597   3.015964     0.36   0.719    -4.827584    6.994778
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.
Nelder-Mead optimization
Initial:      f(p) = -64.896375
Rescale:      f(p) =  -35.86286
Iteration 0:  f(p) =  -35.86286  
Iteration 1:  f(p) =  -32.15977  
Iteration 2:  f(p) =  -32.15977  
Iteration 3:  f(p) =  -32.15977  
Iteration 4:  f(p) =  -32.15977  
Iteration 5:  f(p) = -20.677489  
Iteration 6:  f(p) = -20.677489  
Iteration 7:  f(p) = -11.175434  
Iteration 8:  f(p) = -11.175434  
Iteration 9:  f(p) = -11.175434  
Iteration 10: f(p) = -11.175434  
Iteration 11: f(p) = -11.175434  
Iteration 12: f(p) = -11.175434  
Iteration 13: f(p) = -11.175434  
Iteration 14: f(p) = -10.496079  
Iteration 15: f(p) = -10.496079  
Iteration 16: f(p) = -10.496079  
Iteration 17: f(p) = -10.496079  
Iteration 18: f(p) = -10.496079  
Iteration 19: f(p) = -10.496079  
Iteration 20: f(p) = -10.496079  
Iteration 21: f(p) = -10.496079  
Iteration 22: f(p) = -10.496079  


Quantile Regression for Panel Data (QRPD)
     Number of obs:              1789
     Number of groups:            123
     Min obs per group:             5
     Max obs per group:            18
------------------------------------------------------------------------------
    lrhom_un | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         rol |   .0238984   65.33673     0.00   1.000    -128.0337    128.0815
  infantmort |  -.4801026   1.950949    -0.25   0.806    -4.303893    3.343688
         edu |   .1388345   479.8036     0.00   1.000     -940.259    940.5366
       unemp |   -.980078   2.645827    -0.37   0.711    -6.165803    4.205648
    popdense |   4.905276   4.244059     1.16   0.248    -3.412926    13.22348
    perurban |   3.758071   2.254835     1.67   0.096    -.6613246    8.177466
    sexratio |   2.501623    3.29843     0.76   0.448    -3.963181    8.966428
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.
Nelder-Mead optimization
Initial:      f(p) = -146.23406
Rescale:      f(p) = -29.680374
Iteration 0:  f(p) = -29.680374  
Iteration 1:  f(p) = -5.4944884  
Iteration 2:  f(p) = -5.4944884  
Iteration 3:  f(p) = -5.4944884  
Iteration 4:  f(p) = -5.4944884  
Iteration 5:  f(p) =  -3.296743  
Iteration 6:  f(p) =  -3.296743  
Iteration 7:  f(p) =  -3.296743  
Iteration 8:  f(p) =  -3.296743  
Iteration 9:  f(p) =  -3.296743  
Iteration 10: f(p) =  -3.296743  
Iteration 11: f(p) =  -3.296743  
Iteration 12: f(p) =  -3.296743  
Iteration 13: f(p) = -.76956714  
Iteration 14: f(p) = -.76956714  
Iteration 15: f(p) = -.76956714  
Iteration 16: f(p) = -.76956714  
Iteration 17: f(p) = -.76956714  
Iteration 18: f(p) = -.76956714  
Iteration 19: f(p) = -.76956714  
Iteration 20: f(p) = -.76956714  
Iteration 21: f(p) = -.76956714  


Quantile Regression for Panel Data (QRPD)
     Number of obs:              1789
     Number of groups:            123
     Min obs per group:             5
     Max obs per group:            18
------------------------------------------------------------------------------
    lrhom_un | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         rol |   1.571211          .        .       .            .           .
  infantmort |   .0273723   4.522215     0.01   0.995    -8.836006    8.890751
         edu |  -.5289137          .        .       .            .           .
       unemp |   1.233747   4.052375     0.30   0.761    -6.708762    9.176256
    popdense |   4.902753   4.863235     1.01   0.313    -4.629012    14.43452
    perurban |  -.1024935   1.995912    -0.05   0.959    -4.014409    3.809422
    sexratio |  -.1799933   1.352331    -0.13   0.894    -2.830514    2.470527
------------------------------------------------------------------------------
No excluded instruments - standard QRPD estimation.

. estimates table q3_25 q3_50 q3_75, b(%7.2f) se(%7.2f) p(%4.3f)

--------------------------------------------
    Variable |  q3_25     q3_50     q3_75   
-------------+------------------------------
         rol |    2.11      0.02      1.57  
             |   37.84     65.34      0.00  
             |   0.956     1.000         .  
  infantmort |    5.83     -0.48      0.03  
             |    8.00      1.95      4.52  
             |   0.466     0.806     0.995  
         edu |    1.03      0.14     -0.53  
             |  742.52    479.80      0.00  
             |   0.999     1.000         .  
       unemp |   -1.38     -0.98      1.23  
             |    6.40      2.65      4.05  
             |   0.830     0.711     0.761  
    popdense |    0.92      4.91      4.90  
             |    1.06      4.24      4.86  
             |   0.386     0.248     0.313  
    perurban |   -3.86      3.76     -0.10  
             |    6.22      2.25      2.00  
             |   0.534     0.096     0.959  
    sexratio |    1.08      2.50     -0.18  
             |    3.02      3.30      1.35  
             |   0.719     0.448     0.894  
--------------------------------------------
                              Legend: b/se/p

. 
. //      #4
. //      Exporting results
. esttab q1_25 q1_50 q1_75   q2_25 q2_50 q2_75   q3_25 q3_50 q3_75 using "`file'
> ", replace cells(b se p) 
(file MAR05-QuantModels-UNODC.txt not found)
(output written to MAR05-QuantModels-UNODC.txt)

. 
. //      #5
. //      save and close 
. save `file'.dta, replace 
(file MAR05-QuantModels-UNODC.dta not found)
file MAR05-QuantModels-UNODC.dta saved

. log close 
      name:  <unnamed>
       log:  /Volumes/rdss_meghrogers/Current Research/Markets-Pridemore/Work/MA
> R05-QuantModels-UNODC.log
  log type:  text
 closed on:   5 May 2025, 14:47:02
--------------------------------------------------------------------------------
